EKF-based positioning study of a mobile robot with McNamee wheels

Abstract

Abstract This paper focuses on the localization of a McNamee wheel mobile robot, which has six states of motion and more complex kinematic equations than a normal mobile robot using a McNamee wheel. In this paper, the kinematic model of the McNamee wheel mobile robot is developed, and the extended Kalman filter algorithm (EKF) is used to fuse the inertial measurement unit (IMU) and the wheel encoder odometer information to estimate the position state of the mobile robot and to eliminate the environmental noise and the effect of wheel slippage. By comparing the estimated and calculated values with the actual trajectory, the proposed method is able to eliminate environmental effects and correctly reflect the posture information of the McNamee wheel mobile robot. It is demonstrated that the proposed method can eliminate the environmental influence and correctly reflect the posture information of the McNamee wheel mobile robot.